Convolutional Neural Networks¶
Project: Write an Algorithm for a Dog Identification App¶
In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with '(IMPLEMENTATION)' in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!
Note: Once you have completed all of the code implementations, you need to finalize your work by exporting the Jupyter Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to File -> Download as -> HTML (.html). Include the finished document along with this notebook as your submission.
In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a 'Question X' header. Carefully read each question and provide thorough answers in the following text boxes that begin with 'Answer:'. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.
Note: Code and Markdown cells can be executed using the Shift + Enter keyboard shortcut. Markdown cells can be edited by double-clicking the cell to enter edit mode.
The rubric contains optional "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. If you decide to pursue the "Stand Out Suggestions", you should include the code in this Jupyter notebook.
Why We're Here¶
In this notebook, you will make the first steps towards developing an algorithm that could be used as part of a mobile or web app. At the end of this project, your code will accept any user-supplied image as input. If a dog is detected in the image, it will provide an estimate of the dog's breed. If a human is detected, it will provide an estimate of the dog breed that is most resembling. The image below displays potential sample output of your finished project (... but we expect that each student's algorithm will behave differently!).
In this real-world setting, you will need to piece together a series of models to perform different tasks; for instance, the algorithm that detects humans in an image will be different from the CNN that infers dog breed. There are many points of possible failure, and no perfect algorithm exists. Your imperfect solution will nonetheless create a fun user experience!
The Road Ahead¶
We break the notebook into separate steps. Feel free to use the links below to navigate the notebook.
- Step 0: Import Datasets
- Step 1: Detect Humans
- Step 2: Detect Dogs
- Step 3: Create a CNN to Classify Dog Breeds (from Scratch)
- Step 4: Create a CNN to Classify Dog Breeds (using Transfer Learning)
- Step 5: Write your Algorithm
- Step 6: Test Your Algorithm
Step 0: Import Datasets¶
Make sure that you've downloaded the required human and dog datasets:
Note: if you are using the Udacity workspace, you DO NOT need to re-download these - they can be found in the /data folder as noted in the cell below.
Download the dog dataset. Unzip the folder and place it in this project's home directory, at the location
/dog_images.Download the human dataset. Unzip the folder and place it in the home directory, at location
/lfw.
Note: If you are using a Windows machine, you are encouraged to use 7zip to extract the folder.
In the code cell below, we save the file paths for both the human (LFW) dataset and dog dataset in the numpy arrays human_files and dog_files.
In [1]:
import numpy as np
from glob import glob
# load filenames for human and dog images
human_files = np.array(glob("/data/lfw/*/*"))
dog_files = np.array(glob("/data/dog_images/*/*/*"))
# print number of images in each dataset
print('There are %d total human images.' % len(human_files))
print('There are %d total dog images.' % len(dog_files))
There are 13233 total human images. There are 8351 total dog images.
Step 1: Detect Humans¶
In this section, we use OpenCV's implementation of Haar feature-based cascade classifiers to detect human faces in images.
OpenCV provides many pre-trained face detectors, stored as XML files on github. We have downloaded one of these detectors and stored it in the haarcascades directory. In the next code cell, we demonstrate how to use this detector to find human faces in a sample image.
In [2]:
import cv2
import matplotlib.pyplot as plt
%matplotlib inline
# extract pre-trained face detector
face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml')
# load color (BGR) image
img = cv2.imread(human_files[0])
# convert BGR image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# find faces in image
faces = face_cascade.detectMultiScale(gray)
# print number of faces detected in the image
print('Number of faces detected:', len(faces))
# get bounding box for each detected face
for (x,y,w,h) in faces:
# add bounding box to color image
cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
# convert BGR image to RGB for plotting
cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# display the image, along with bounding box
plt.imshow(cv_rgb)
plt.show()
Number of faces detected: 1
Before using any of the face detectors, it is standard procedure to convert the images to grayscale. The detectMultiScale function executes the classifier stored in face_cascade and takes the grayscale image as a parameter.
In the above code, faces is a numpy array of detected faces, where each row corresponds to a detected face. Each detected face is a 1D array with four entries that specifies the bounding box of the detected face. The first two entries in the array (extracted in the above code as x and y) specify the horizontal and vertical positions of the top left corner of the bounding box. The last two entries in the array (extracted here as w and h) specify the width and height of the box.
Write a Human Face Detector¶
We can use this procedure to write a function that returns True if a human face is detected in an image and False otherwise. This function, aptly named face_detector, takes a string-valued file path to an image as input and appears in the code block below.
In [3]:
# returns "True" if face is detected in image stored at img_path
def face_detector(img_path):
img = cv2.imread(img_path)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray)
return len(faces) > 0
(IMPLEMENTATION) Assess the Human Face Detector¶
Question 1: Use the code cell below to test the performance of the face_detector function.
- What percentage of the first 100 images in
human_fileshave a detected human face? - What percentage of the first 100 images in
dog_fileshave a detected human face?
Ideally, we would like 100% of human images with a detected face and 0% of dog images with a detected face. You will see that our algorithm falls short of this goal, but still gives acceptable performance. We extract the file paths for the first 100 images from each of the datasets and store them in the numpy arrays human_files_short and dog_files_short.
Answer: (You can print out your results and/or write your percentages in this cell)
In [4]:
from tqdm import tqdm
human_files_short = human_files[:100]
dog_files_short = dog_files[:100]
#-#-# Do NOT modify the code above this line. #-#-#
## TODO: Test the performance of the face_detector algorithm
## on the images in human_files_short and dog_files_short.
totalHumFaces = 0
for humfile in human_files_short:
if(face_detector(humfile)):
totalHumFaces = totalHumFaces +1
print("Percentage of Human faces detected :",totalHumFaces/len(human_files_short)*100)
totalDogFaces = 0
for dogfile in dog_files_short:
if(face_detector(dogfile)):
totalDogFaces = totalDogFaces +1
print("Percentage of Dog faces detected :",totalDogFaces/len(dog_files_short)*100)
Percentage of Human faces detected : 98.0 Percentage of Dog faces detected : 17.0
We suggest the face detector from OpenCV as a potential way to detect human images in your algorithm, but you are free to explore other approaches, especially approaches that make use of deep learning :). Please use the code cell below to design and test your own face detection algorithm. If you decide to pursue this optional task, report performance on human_files_short and dog_files_short.
In [5]:
### (Optional)
### TODO: Test performance of anotherface detection algorithm.
### Feel free to use as many code cells as needed.
Step 2: Detect Dogs¶
In this section, we use a pre-trained model to detect dogs in images.
Obtain Pre-trained VGG-16 Model¶
The code cell below downloads the VGG-16 model, along with weights that have been trained on ImageNet, a very large, very popular dataset used for image classification and other vision tasks. ImageNet contains over 10 million URLs, each linking to an image containing an object from one of 1000 categories.
In [6]:
import torch
import torchvision.models as models
# define VGG16 model
VGG16 = models.vgg16(pretrained=True)
# check if CUDA is available
use_cuda = torch.cuda.is_available()
# move model to GPU if CUDA is available
if use_cuda:
VGG16 = VGG16.cuda()
Downloading: "https://download.pytorch.org/models/vgg16-397923af.pth" to /root/.torch/models/vgg16-397923af.pth 100%|██████████| 553433881/553433881 [00:10<00:00, 50425050.27it/s]
Given an image, this pre-trained VGG-16 model returns a prediction (derived from the 1000 possible categories in ImageNet) for the object that is contained in the image.
(IMPLEMENTATION) Making Predictions with a Pre-trained Model¶
In the next code cell, you will write a function that accepts a path to an image (such as 'dogImages/train/001.Affenpinscher/Affenpinscher_00001.jpg') as input and returns the index corresponding to the ImageNet class that is predicted by the pre-trained VGG-16 model. The output should always be an integer between 0 and 999, inclusive.
Before writing the function, make sure that you take the time to learn how to appropriately pre-process tensors for pre-trained models in the PyTorch documentation.
In [7]:
from PIL import Image
import torchvision.transforms as transforms
def VGG16_predict(img_path):
'''
Use pre-trained VGG-16 model to obtain index corresponding to
predicted ImageNet class for image at specified path
Args:
img_path: path to an image
Returns:
Index corresponding to VGG-16 model's prediction
'''
## TODO: Complete the function.
## Load and pre-process an image from the given img_path
## Return the *index* of the predicted class for that image
input_image = Image.open(img_path)
preprocess = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
input_tensor = preprocess(input_image)
input_batch = input_tensor.unsqueeze(0)
if use_cuda:
input_batch = input_batch.cuda()
output = VGG16(input_batch)
print(output)
print(output[0])
probabilities = torch.nn.functional.softmax(output[0], dim=0)
probabilities = probabilities.cpu()
probabilities = probabilities.detach().numpy()
ind = np.argmax(probabilities)
#return probabilities # predicted class index
return ind
(IMPLEMENTATION) Write a Dog Detector¶
While looking at the dictionary, you will notice that the categories corresponding to dogs appear in an uninterrupted sequence and correspond to dictionary keys 151-268, inclusive, to include all categories from 'Chihuahua' to 'Mexican hairless'. Thus, in order to check to see if an image is predicted to contain a dog by the pre-trained VGG-16 model, we need only check if the pre-trained model predicts an index between 151 and 268 (inclusive).
Use these ideas to complete the dog_detector function below, which returns True if a dog is detected in an image (and False if not).
In [8]:
### returns "True" if a dog is detected in the image stored at img_path
def dog_detector(img_path):
## TODO: Complete the function.
detIndex = VGG16_predict(img_path)
print('detected index :',detIndex)
if(151<=detIndex<=268):
return True
return False # true/false
(IMPLEMENTATION) Assess the Dog Detector¶
Question 2: Use the code cell below to test the performance of your dog_detector function.
- What percentage of the images in
human_files_shorthave a detected dog? - What percentage of the images in
dog_files_shorthave a detected dog?
Answer:
In [9]:
### TODO: Test the performance of the dog_detector function
### on the images in human_files_short and dog_files_short.
totalHumFaces = 0
for humfile in human_files_short:
if(dog_detector(humfile)):
totalHumFaces = totalHumFaces +1
print("Percentage of Human faces detected :",totalHumFaces/len(human_files_short)*100)
totalDogFaces = 0
for dogfile in dog_files_short:
if(dog_detector(dogfile)):
totalDogFaces = totalDogFaces +1
print("Percentage of Dog faces detected :",totalDogFaces/len(dog_files_short)*100)
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detected index : 906
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detected index : 906
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detected index : 823
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detected index : 432
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detected index : 834
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detected index : 906
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detected index : 906
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detected index : 834
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detected index : 903
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detected index : 906
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detected index : 652
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detected index : 683
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detected index : 834
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detected index : 683
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detected index : 683
Percentage of Human faces detected : 1.0
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detected index : 236
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0.5933, 1.1475, 1.6474, -1.4020, -0.9884, 0.1324,
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2.3441, -0.0652, 0.7011, 0.5394, -1.2810, 0.4311,
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0.6643, 1.5874, -1.2012, 0.3774, -2.8924, -0.9054,
1.1026, -0.5456, 1.5667, -1.4184, -1.2977, 1.4958,
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0.3115, 0.0420, 0.8352, 0.1411, -0.2497, 2.0102,
-1.0358, 0.2602, -0.0596, -0.2040, 1.3498, -1.7371,
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0.0036, -3.8088, 2.5529, 0.7002, -0.7657, -1.0511,
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-0.8890, -0.9890, 0.1886, 4.2057, -2.1919, 1.9531,
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0.6236, -0.6646, -2.9415, 0.0235, -0.8640, 0.3261,
-0.7341, -2.9998, 0.4249, -0.0587], device='cuda:0')
detected index : 236
Percentage of Dog faces detected : 100.0
We suggest VGG-16 as a potential network to detect dog images in your algorithm, but you are free to explore other pre-trained networks (such as Inception-v3, ResNet-50, etc). Please use the code cell below to test other pre-trained PyTorch models. If you decide to pursue this optional task, report performance on human_files_short and dog_files_short.
In [10]:
### (Optional)
### TODO: Report the performance of another pre-trained network.
### Feel free to use as many code cells as needed.
Step 3: Create a CNN to Classify Dog Breeds (from Scratch)¶
Now that we have functions for detecting humans and dogs in images, we need a way to predict breed from images. In this step, you will create a CNN that classifies dog breeds. You must create your CNN from scratch (so, you can't use transfer learning yet!), and you must attain a test accuracy of at least 10%. In Step 4 of this notebook, you will have the opportunity to use transfer learning to create a CNN that attains greatly improved accuracy.
We mention that the task of assigning breed to dogs from images is considered exceptionally challenging. To see why, consider that even a human would have trouble distinguishing between a Brittany and a Welsh Springer Spaniel.
| Brittany | Welsh Springer Spaniel |
|---|---|
 |
 |
It is not difficult to find other dog breed pairs with minimal inter-class variation (for instance, Curly-Coated Retrievers and American Water Spaniels).
| Curly-Coated Retriever | American Water Spaniel |
|---|---|
 |
 |
Likewise, recall that labradors come in yellow, chocolate, and black. Your vision-based algorithm will have to conquer this high intra-class variation to determine how to classify all of these different shades as the same breed.
| Yellow Labrador | Chocolate Labrador | Black Labrador |
|---|---|---|
 |
 |
 |
We also mention that random chance presents an exceptionally low bar: setting aside the fact that the classes are slightly imabalanced, a random guess will provide a correct answer roughly 1 in 133 times, which corresponds to an accuracy of less than 1%.
Remember that the practice is far ahead of the theory in deep learning. Experiment with many different architectures, and trust your intuition. And, of course, have fun!
(IMPLEMENTATION) Specify Data Loaders for the Dog Dataset¶
Use the code cell below to write three separate data loaders for the training, validation, and test datasets of dog images (located at dog_images/train, dog_images/valid, and dog_images/test, respectively). You may find this documentation on custom datasets to be a useful resource. If you are interested in augmenting your training and/or validation data, check out the wide variety of transforms!
In [11]:
import os
from torchvision import datasets
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.dataset import Dataset
### TODO: Write data loaders for training, validation, and test sets
## Specify appropriate transforms, and batch_sizes
batch_size=64
num_workers = 0
data_dir = '/data/dog_images/'
train_dir = os.path.join(data_dir, 'train/')
valid_dir = os.path.join(data_dir, 'valid/')
test_dir = os.path.join(data_dir, 'test/')
In [12]:
standard_normalization = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
In [13]:
data_transforms = {'train': transforms.Compose([transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
standard_normalization]),
'val': transforms.Compose([transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
standard_normalization]),
'test': transforms.Compose([transforms.Resize(size=(224,224)),
transforms.ToTensor(),
standard_normalization])
}
In [14]:
train_data = datasets.ImageFolder(train_dir, transform=data_transforms['train'])
valid_data = datasets.ImageFolder(valid_dir, transform=data_transforms['val'])
test_data = datasets.ImageFolder(test_dir, transform=data_transforms['test'])
In [15]:
train_loader = torch.utils.data.DataLoader(train_data,
batch_size=batch_size,
num_workers=num_workers,
shuffle=True)
valid_loader = torch.utils.data.DataLoader(valid_data,
batch_size=batch_size,
num_workers=num_workers,
shuffle=False)
test_loader = torch.utils.data.DataLoader(test_data,
batch_size=batch_size,
num_workers=num_workers,
shuffle=False)
loaders_scratch = {
'train': train_loader,
'valid': valid_loader,
'test': test_loader
}
Question 3: Describe your chosen procedure for preprocessing the data.
- How does your code resize the images (by cropping, stretching, etc)? What size did you pick for the input tensor, and why?
- Did you decide to augment the dataset? If so, how (through translations, flips, rotations, etc)? If not, why not?
Answer:
In [16]:
class Flatten(torch.nn.Module):
def forward(self, x):
batch_size = x.shape[0]
return x.view(batch_size, -1)
(IMPLEMENTATION) Model Architecture¶
Create a CNN to classify dog breed. Use the template in the code cell below.
In [17]:
import torch.nn as nn
import torch.nn.functional as F
# define the CNN architecture
class Net(nn.Module):
### TODO: choose an architecture, and complete the class
def __init__(self):
super(Net, self).__init__()
## Define layers of a CNN
self.conv1 = nn.Conv2d(3, 32, 3, stride=2, padding=1)
self.conv2 = nn.Conv2d(32, 64, 3, stride=2, padding=1)
self.conv3 = nn.Conv2d(64, 128, 3, padding=1)
# pool
self.pool = nn.MaxPool2d(2, 2)
# fully-connected
self.fc1 = nn.Linear(7*7*128, 500)
self.fc2 = nn.Linear(500, 133)
# drop-out
self.dropout = nn.Dropout(0.3)
def forward(self, x):
## Define forward behavior
x = F.relu(self.conv1(x))
x = self.pool(x) # 32 * 16 * 16
x = F.relu(self.conv2(x))
x = self.pool(x) # 64 * 8 * 8
x = F.relu(self.conv3(x))
x = self.pool(x) # 128 * 4 * 4
# flatten
#x = x.view(-1, 7*7*128)
x = Flatten().forward(x)
x = self.dropout(x)
x = F.relu(self.fc1(x))
x = self.dropout(x)
x = self.fc2(x)
return x
#-#-# You so NOT have to modify the code below this line. #-#-#
# instantiate the CNN
model_scratch = Net()
# move tensors to GPU if CUDA is available
if use_cuda:
model_scratch.cuda()
Question 4: Outline the steps you took to get to your final CNN architecture and your reasoning at each step.
Answer:
(IMPLEMENTATION) Specify Loss Function and Optimizer¶
Use the next code cell to specify a loss function and optimizer. Save the chosen loss function as criterion_scratch, and the optimizer as optimizer_scratch below.
In [18]:
import torch.optim as optim
import torch.nn as nn
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
### TODO: select loss function
criterion_scratch = nn.CrossEntropyLoss()
### TODO: select optimizer
optimizer_scratch = optim.Adam(model_scratch.parameters(), lr=0.001)
(IMPLEMENTATION) Train and Validate the Model¶
Train and validate your model in the code cell below. Save the final model parameters at filepath 'model_scratch.pt'.
In [19]:
def train(n_epochs, loaders, model, optimizer, criterion, use_cuda, save_path):
"""returns trained model"""
# initialize tracker for minimum validation loss
valid_loss_min = np.Inf
for epoch in range(1, n_epochs+1):
# initialize variables to monitor training and validation loss
train_loss = 0.0
valid_loss = 0.0
###################
# train the model #
###################
model.train()
for batch_idx, (data, target) in enumerate(loaders['train']):
optimizer.zero_grad()
# move to GPU
if use_cuda:
data, target = data.cuda(), target.cuda()
## find the loss and update the model parameters accordingly
## record the average training loss, using something like
## train_loss = train_loss + ((1 / (batch_idx + 1)) * (loss.data - train_loss))
output = model(data)
loss = criterion_scratch(output,target)
loss.backward()
optimizer.step()
#train_loss += loss
train_loss = train_loss + ((1 / (batch_idx + 1)) * (loss.data - train_loss))
if batch_idx % 100 == 0:
print('Epoch %d, Batch %d loss: %.6f' %
(epoch, batch_idx + 1, train_loss))
######################
# validate the model #
######################
model.eval()
for batch_idx, (data, target) in enumerate(loaders['valid']):
# move to GPU
if use_cuda:
data, target = data.cuda(), target.cuda()
## update the average validation loss
output = model(data)
loss = criterion_scratch(output,target)
#valid_loss += loss
valid_loss += ((1 / (batch_idx + 1)) * (loss.data - valid_loss))
# print training/validation statistics
print('Epoch: {} \tTraining Loss: {:.6f} \tValidation Loss: {:.6f}'.format(
epoch,
train_loss,
valid_loss
))
## TODO: save the model if validation loss has decreased
if valid_loss <= valid_loss_min:
print('Validation loss decreased ({:.6f} --> {:.6f}). Saving model ...'.format(
valid_loss_min,
valid_loss))
#Saving the model
torch.save(model.state_dict(), 'model_scratch.pt')
valid_loss_min = valid_loss
# return trained model
return model
# train the model
model_scratch = train(25, loaders_scratch, model_scratch, optimizer_scratch,
criterion_scratch, use_cuda, 'model_scratch.pt')
# load the model that got the best validation accuracy
model_scratch.load_state_dict(torch.load('model_scratch.pt'))
Epoch 1, Batch 1 loss: 4.887196 Epoch 1, Batch 101 loss: 4.840675 Epoch: 1 Training Loss: 4.835996 Validation Loss: 4.486845 Epoch: 1 Training Loss: 4.835996 Validation Loss: 4.486599 Epoch: 1 Training Loss: 4.835996 Validation Loss: 4.427537 Epoch: 1 Training Loss: 4.835996 Validation Loss: 4.446588 Epoch: 1 Training Loss: 4.835996 Validation Loss: 4.483377 Epoch: 1 Training Loss: 4.835996 Validation Loss: 4.516126 Epoch: 1 Training Loss: 4.835996 Validation Loss: 4.507284 Epoch: 1 Training Loss: 4.835996 Validation Loss: 4.526196 Epoch: 1 Training Loss: 4.835996 Validation Loss: 4.555621 Epoch: 1 Training Loss: 4.835996 Validation Loss: 4.563366 Epoch: 1 Training Loss: 4.835996 Validation Loss: 4.594037 Epoch: 1 Training Loss: 4.835996 Validation Loss: 4.607028 Epoch: 1 Training Loss: 4.835996 Validation Loss: 4.631314 Epoch: 1 Training Loss: 4.835996 Validation Loss: 4.634785 Validation loss decreased (inf --> 4.634785). Saving model ... Epoch 2, Batch 1 loss: 4.685824 Epoch 2, Batch 101 loss: 4.603261 Epoch: 2 Training Loss: 4.599709 Validation Loss: 4.272058 Epoch: 2 Training Loss: 4.599709 Validation Loss: 4.253984 Epoch: 2 Training Loss: 4.599709 Validation Loss: 4.090218 Epoch: 2 Training Loss: 4.599709 Validation Loss: 4.128248 Epoch: 2 Training Loss: 4.599709 Validation Loss: 4.165036 Epoch: 2 Training Loss: 4.599709 Validation Loss: 4.236329 Epoch: 2 Training Loss: 4.599709 Validation Loss: 4.243763 Epoch: 2 Training Loss: 4.599709 Validation Loss: 4.290229 Epoch: 2 Training Loss: 4.599709 Validation Loss: 4.319910 Epoch: 2 Training Loss: 4.599709 Validation Loss: 4.324542 Epoch: 2 Training Loss: 4.599709 Validation Loss: 4.365741 Epoch: 2 Training Loss: 4.599709 Validation Loss: 4.381355 Epoch: 2 Training Loss: 4.599709 Validation Loss: 4.410904 Epoch: 2 Training Loss: 4.599709 Validation Loss: 4.433503 Validation loss decreased (4.634785 --> 4.433503). Saving model ... Epoch 3, Batch 1 loss: 4.664956 Epoch 3, Batch 101 loss: 4.482509 Epoch: 3 Training Loss: 4.480130 Validation Loss: 4.395438 Epoch: 3 Training Loss: 4.480130 Validation Loss: 4.261808 Epoch: 3 Training Loss: 4.480130 Validation Loss: 4.091146 Epoch: 3 Training Loss: 4.480130 Validation Loss: 4.097519 Epoch: 3 Training Loss: 4.480130 Validation Loss: 4.156881 Epoch: 3 Training Loss: 4.480130 Validation Loss: 4.213216 Epoch: 3 Training Loss: 4.480130 Validation Loss: 4.218166 Epoch: 3 Training Loss: 4.480130 Validation Loss: 4.225544 Epoch: 3 Training Loss: 4.480130 Validation Loss: 4.247872 Epoch: 3 Training Loss: 4.480130 Validation Loss: 4.249625 Epoch: 3 Training Loss: 4.480130 Validation Loss: 4.292315 Epoch: 3 Training Loss: 4.480130 Validation Loss: 4.310225 Epoch: 3 Training Loss: 4.480130 Validation Loss: 4.326940 Epoch: 3 Training Loss: 4.480130 Validation Loss: 4.337039 Validation loss decreased (4.433503 --> 4.337039). Saving model ... Epoch 4, Batch 1 loss: 4.546309 Epoch 4, Batch 101 loss: 4.347517 Epoch: 4 Training Loss: 4.345898 Validation Loss: 4.148676 Epoch: 4 Training Loss: 4.345898 Validation Loss: 4.030264 Epoch: 4 Training Loss: 4.345898 Validation Loss: 3.817381 Epoch: 4 Training Loss: 4.345898 Validation Loss: 3.865083 Epoch: 4 Training Loss: 4.345898 Validation Loss: 3.958956 Epoch: 4 Training Loss: 4.345898 Validation Loss: 4.054182 Epoch: 4 Training Loss: 4.345898 Validation Loss: 4.064449 Epoch: 4 Training Loss: 4.345898 Validation Loss: 4.071179 Epoch: 4 Training Loss: 4.345898 Validation Loss: 4.118142 Epoch: 4 Training Loss: 4.345898 Validation Loss: 4.129253 Epoch: 4 Training Loss: 4.345898 Validation Loss: 4.168468 Epoch: 4 Training Loss: 4.345898 Validation Loss: 4.192839 Epoch: 4 Training Loss: 4.345898 Validation Loss: 4.214878 Epoch: 4 Training Loss: 4.345898 Validation Loss: 4.234280 Validation loss decreased (4.337039 --> 4.234280). Saving model ... Epoch 5, Batch 1 loss: 4.289979 Epoch 5, Batch 101 loss: 4.227940 Epoch: 5 Training Loss: 4.228206 Validation Loss: 3.994769 Epoch: 5 Training Loss: 4.228206 Validation Loss: 3.913906 Epoch: 5 Training Loss: 4.228206 Validation Loss: 3.714613 Epoch: 5 Training Loss: 4.228206 Validation Loss: 3.706826 Epoch: 5 Training Loss: 4.228206 Validation Loss: 3.799565 Epoch: 5 Training Loss: 4.228206 Validation Loss: 3.911345 Epoch: 5 Training Loss: 4.228206 Validation Loss: 3.898159 Epoch: 5 Training Loss: 4.228206 Validation Loss: 3.911512 Epoch: 5 Training Loss: 4.228206 Validation Loss: 3.932104 Epoch: 5 Training Loss: 4.228206 Validation Loss: 3.947966 Epoch: 5 Training Loss: 4.228206 Validation Loss: 3.983532 Epoch: 5 Training Loss: 4.228206 Validation Loss: 4.010183 Epoch: 5 Training Loss: 4.228206 Validation Loss: 4.037040 Epoch: 5 Training Loss: 4.228206 Validation Loss: 4.073637 Validation loss decreased (4.234280 --> 4.073637). Saving model ... Epoch 6, Batch 1 loss: 4.027404 Epoch 6, Batch 101 loss: 4.145639 Epoch: 6 Training Loss: 4.144106 Validation Loss: 4.126142 Epoch: 6 Training Loss: 4.144106 Validation Loss: 3.967678 Epoch: 6 Training Loss: 4.144106 Validation Loss: 3.730183 Epoch: 6 Training Loss: 4.144106 Validation Loss: 3.699884 Epoch: 6 Training Loss: 4.144106 Validation Loss: 3.773376 Epoch: 6 Training Loss: 4.144106 Validation Loss: 3.876260 Epoch: 6 Training Loss: 4.144106 Validation Loss: 3.854711 Epoch: 6 Training Loss: 4.144106 Validation Loss: 3.866251 Epoch: 6 Training Loss: 4.144106 Validation Loss: 3.894777 Epoch: 6 Training Loss: 4.144106 Validation Loss: 3.886132 Epoch: 6 Training Loss: 4.144106 Validation Loss: 3.909235 Epoch: 6 Training Loss: 4.144106 Validation Loss: 3.934210 Epoch: 6 Training Loss: 4.144106 Validation Loss: 3.963273 Epoch: 6 Training Loss: 4.144106 Validation Loss: 4.002864 Validation loss decreased (4.073637 --> 4.002864). Saving model ... Epoch 7, Batch 1 loss: 4.277346 Epoch 7, Batch 101 loss: 4.071037 Epoch: 7 Training Loss: 4.069731 Validation Loss: 4.023105 Epoch: 7 Training Loss: 4.069731 Validation Loss: 3.877351 Epoch: 7 Training Loss: 4.069731 Validation Loss: 3.642400 Epoch: 7 Training Loss: 4.069731 Validation Loss: 3.605013 Epoch: 7 Training Loss: 4.069731 Validation Loss: 3.681026 Epoch: 7 Training Loss: 4.069731 Validation Loss: 3.778046 Epoch: 7 Training Loss: 4.069731 Validation Loss: 3.772437 Epoch: 7 Training Loss: 4.069731 Validation Loss: 3.764445 Epoch: 7 Training Loss: 4.069731 Validation Loss: 3.783985 Epoch: 7 Training Loss: 4.069731 Validation Loss: 3.781054 Epoch: 7 Training Loss: 4.069731 Validation Loss: 3.817930 Epoch: 7 Training Loss: 4.069731 Validation Loss: 3.846552 Epoch: 7 Training Loss: 4.069731 Validation Loss: 3.873975 Epoch: 7 Training Loss: 4.069731 Validation Loss: 3.902792 Validation loss decreased (4.002864 --> 3.902792). Saving model ... Epoch 8, Batch 1 loss: 4.242698 Epoch 8, Batch 101 loss: 4.013954 Epoch: 8 Training Loss: 4.014043 Validation Loss: 4.033944 Epoch: 8 Training Loss: 4.014043 Validation Loss: 3.820993 Epoch: 8 Training Loss: 4.014043 Validation Loss: 3.597839 Epoch: 8 Training Loss: 4.014043 Validation Loss: 3.545689 Epoch: 8 Training Loss: 4.014043 Validation Loss: 3.626241 Epoch: 8 Training Loss: 4.014043 Validation Loss: 3.725709 Epoch: 8 Training Loss: 4.014043 Validation Loss: 3.672869 Epoch: 8 Training Loss: 4.014043 Validation Loss: 3.654327 Epoch: 8 Training Loss: 4.014043 Validation Loss: 3.678276 Epoch: 8 Training Loss: 4.014043 Validation Loss: 3.681393 Epoch: 8 Training Loss: 4.014043 Validation Loss: 3.734522 Epoch: 8 Training Loss: 4.014043 Validation Loss: 3.765653 Epoch: 8 Training Loss: 4.014043 Validation Loss: 3.787767 Epoch: 8 Training Loss: 4.014043 Validation Loss: 3.826230 Validation loss decreased (3.902792 --> 3.826230). Saving model ... Epoch 9, Batch 1 loss: 3.867110 Epoch 9, Batch 101 loss: 3.927982 Epoch: 9 Training Loss: 3.928421 Validation Loss: 3.780118 Epoch: 9 Training Loss: 3.928421 Validation Loss: 3.634651 Epoch: 9 Training Loss: 3.928421 Validation Loss: 3.478472 Epoch: 9 Training Loss: 3.928421 Validation Loss: 3.514278 Epoch: 9 Training Loss: 3.928421 Validation Loss: 3.582087 Epoch: 9 Training Loss: 3.928421 Validation Loss: 3.646340 Epoch: 9 Training Loss: 3.928421 Validation Loss: 3.658441 Epoch: 9 Training Loss: 3.928421 Validation Loss: 3.713572 Epoch: 9 Training Loss: 3.928421 Validation Loss: 3.711526 Epoch: 9 Training Loss: 3.928421 Validation Loss: 3.736066 Epoch: 9 Training Loss: 3.928421 Validation Loss: 3.790426 Epoch: 9 Training Loss: 3.928421 Validation Loss: 3.789634 Epoch: 9 Training Loss: 3.928421 Validation Loss: 3.811520 Epoch: 9 Training Loss: 3.928421 Validation Loss: 3.853019 Epoch 10, Batch 1 loss: 3.933859 Epoch 10, Batch 101 loss: 3.871788 Epoch: 10 Training Loss: 3.874080 Validation Loss: 3.729915 Epoch: 10 Training Loss: 3.874080 Validation Loss: 3.567893 Epoch: 10 Training Loss: 3.874080 Validation Loss: 3.436624 Epoch: 10 Training Loss: 3.874080 Validation Loss: 3.479907 Epoch: 10 Training Loss: 3.874080 Validation Loss: 3.532553 Epoch: 10 Training Loss: 3.874080 Validation Loss: 3.624586 Epoch: 10 Training Loss: 3.874080 Validation Loss: 3.626892 Epoch: 10 Training Loss: 3.874080 Validation Loss: 3.672436 Epoch: 10 Training Loss: 3.874080 Validation Loss: 3.668901 Epoch: 10 Training Loss: 3.874080 Validation Loss: 3.678948 Epoch: 10 Training Loss: 3.874080 Validation Loss: 3.727073 Epoch: 10 Training Loss: 3.874080 Validation Loss: 3.715541 Epoch: 10 Training Loss: 3.874080 Validation Loss: 3.744169 Epoch: 10 Training Loss: 3.874080 Validation Loss: 3.772901 Validation loss decreased (3.826230 --> 3.772901). Saving model ... Epoch 11, Batch 1 loss: 3.908168 Epoch 11, Batch 101 loss: 3.810587 Epoch: 11 Training Loss: 3.804158 Validation Loss: 3.911507 Epoch: 11 Training Loss: 3.804158 Validation Loss: 3.659879 Epoch: 11 Training Loss: 3.804158 Validation Loss: 3.460905 Epoch: 11 Training Loss: 3.804158 Validation Loss: 3.417838 Epoch: 11 Training Loss: 3.804158 Validation Loss: 3.478001 Epoch: 11 Training Loss: 3.804158 Validation Loss: 3.543348 Epoch: 11 Training Loss: 3.804158 Validation Loss: 3.496780 Epoch: 11 Training Loss: 3.804158 Validation Loss: 3.507042 Epoch: 11 Training Loss: 3.804158 Validation Loss: 3.507631 Epoch: 11 Training Loss: 3.804158 Validation Loss: 3.502984 Epoch: 11 Training Loss: 3.804158 Validation Loss: 3.551944 Epoch: 11 Training Loss: 3.804158 Validation Loss: 3.588192 Epoch: 11 Training Loss: 3.804158 Validation Loss: 3.595842 Epoch: 11 Training Loss: 3.804158 Validation Loss: 3.610720 Validation loss decreased (3.772901 --> 3.610720). Saving model ... Epoch 12, Batch 1 loss: 3.616525 Epoch 12, Batch 101 loss: 3.738408 Epoch: 12 Training Loss: 3.741038 Validation Loss: 3.812819 Epoch: 12 Training Loss: 3.741038 Validation Loss: 3.679513 Epoch: 12 Training Loss: 3.741038 Validation Loss: 3.530666 Epoch: 12 Training Loss: 3.741038 Validation Loss: 3.468411 Epoch: 12 Training Loss: 3.741038 Validation Loss: 3.534113 Epoch: 12 Training Loss: 3.741038 Validation Loss: 3.620121 Epoch: 12 Training Loss: 3.741038 Validation Loss: 3.588746 Epoch: 12 Training Loss: 3.741038 Validation Loss: 3.618381 Epoch: 12 Training Loss: 3.741038 Validation Loss: 3.610457 Epoch: 12 Training Loss: 3.741038 Validation Loss: 3.594445 Epoch: 12 Training Loss: 3.741038 Validation Loss: 3.629528 Epoch: 12 Training Loss: 3.741038 Validation Loss: 3.664797 Epoch: 12 Training Loss: 3.741038 Validation Loss: 3.681243 Epoch: 12 Training Loss: 3.741038 Validation Loss: 3.706573 Epoch 13, Batch 1 loss: 3.581602 Epoch 13, Batch 101 loss: 3.690518 Epoch: 13 Training Loss: 3.688136 Validation Loss: 3.899276 Epoch: 13 Training Loss: 3.688136 Validation Loss: 3.528118 Epoch: 13 Training Loss: 3.688136 Validation Loss: 3.324928 Epoch: 13 Training Loss: 3.688136 Validation Loss: 3.284623 Epoch: 13 Training Loss: 3.688136 Validation Loss: 3.407860 Epoch: 13 Training Loss: 3.688136 Validation Loss: 3.489754 Epoch: 13 Training Loss: 3.688136 Validation Loss: 3.476486 Epoch: 13 Training Loss: 3.688136 Validation Loss: 3.482143 Epoch: 13 Training Loss: 3.688136 Validation Loss: 3.493517 Epoch: 13 Training Loss: 3.688136 Validation Loss: 3.494897 Epoch: 13 Training Loss: 3.688136 Validation Loss: 3.545396 Epoch: 13 Training Loss: 3.688136 Validation Loss: 3.571325 Epoch: 13 Training Loss: 3.688136 Validation Loss: 3.587023 Epoch: 13 Training Loss: 3.688136 Validation Loss: 3.585237 Validation loss decreased (3.610720 --> 3.585237). Saving model ... Epoch 14, Batch 1 loss: 3.471951 Epoch 14, Batch 101 loss: 3.658508 Epoch: 14 Training Loss: 3.655514 Validation Loss: 3.742926 Epoch: 14 Training Loss: 3.655514 Validation Loss: 3.421272 Epoch: 14 Training Loss: 3.655514 Validation Loss: 3.188082 Epoch: 14 Training Loss: 3.655514 Validation Loss: 3.194553 Epoch: 14 Training Loss: 3.655514 Validation Loss: 3.317825 Epoch: 14 Training Loss: 3.655514 Validation Loss: 3.403980 Epoch: 14 Training Loss: 3.655514 Validation Loss: 3.393965 Epoch: 14 Training Loss: 3.655514 Validation Loss: 3.406065 Epoch: 14 Training Loss: 3.655514 Validation Loss: 3.401911 Epoch: 14 Training Loss: 3.655514 Validation Loss: 3.388477 Epoch: 14 Training Loss: 3.655514 Validation Loss: 3.459013 Epoch: 14 Training Loss: 3.655514 Validation Loss: 3.477558 Epoch: 14 Training Loss: 3.655514 Validation Loss: 3.492474 Epoch: 14 Training Loss: 3.655514 Validation Loss: 3.537662 Validation loss decreased (3.585237 --> 3.537662). Saving model ... Epoch 15, Batch 1 loss: 3.450675 Epoch 15, Batch 101 loss: 3.626417 Epoch: 15 Training Loss: 3.630700 Validation Loss: 3.709033 Epoch: 15 Training Loss: 3.630700 Validation Loss: 3.339523 Epoch: 15 Training Loss: 3.630700 Validation Loss: 3.201381 Epoch: 15 Training Loss: 3.630700 Validation Loss: 3.165719 Epoch: 15 Training Loss: 3.630700 Validation Loss: 3.283895 Epoch: 15 Training Loss: 3.630700 Validation Loss: 3.385730 Epoch: 15 Training Loss: 3.630700 Validation Loss: 3.364126 Epoch: 15 Training Loss: 3.630700 Validation Loss: 3.354747 Epoch: 15 Training Loss: 3.630700 Validation Loss: 3.357265 Epoch: 15 Training Loss: 3.630700 Validation Loss: 3.369168 Epoch: 15 Training Loss: 3.630700 Validation Loss: 3.436617 Epoch: 15 Training Loss: 3.630700 Validation Loss: 3.458237 Epoch: 15 Training Loss: 3.630700 Validation Loss: 3.474036 Epoch: 15 Training Loss: 3.630700 Validation Loss: 3.503493 Validation loss decreased (3.537662 --> 3.503493). Saving model ... Epoch 16, Batch 1 loss: 3.365396 Epoch 16, Batch 101 loss: 3.563277 Epoch: 16 Training Loss: 3.568012 Validation Loss: 3.554491 Epoch: 16 Training Loss: 3.568012 Validation Loss: 3.364110 Epoch: 16 Training Loss: 3.568012 Validation Loss: 3.203831 Epoch: 16 Training Loss: 3.568012 Validation Loss: 3.258629 Epoch: 16 Training Loss: 3.568012 Validation Loss: 3.380975 Epoch: 16 Training Loss: 3.568012 Validation Loss: 3.464527 Epoch: 16 Training Loss: 3.568012 Validation Loss: 3.428534 Epoch: 16 Training Loss: 3.568012 Validation Loss: 3.433272 Epoch: 16 Training Loss: 3.568012 Validation Loss: 3.388042 Epoch: 16 Training Loss: 3.568012 Validation Loss: 3.379221 Epoch: 16 Training Loss: 3.568012 Validation Loss: 3.436758 Epoch: 16 Training Loss: 3.568012 Validation Loss: 3.457858 Epoch: 16 Training Loss: 3.568012 Validation Loss: 3.473218 Epoch: 16 Training Loss: 3.568012 Validation Loss: 3.507708 Epoch 17, Batch 1 loss: 3.810685 Epoch 17, Batch 101 loss: 3.562617 Epoch: 17 Training Loss: 3.563430 Validation Loss: 3.469339 Epoch: 17 Training Loss: 3.563430 Validation Loss: 3.258611 Epoch: 17 Training Loss: 3.563430 Validation Loss: 3.180005 Epoch: 17 Training Loss: 3.563430 Validation Loss: 3.194233 Epoch: 17 Training Loss: 3.563430 Validation Loss: 3.278169 Epoch: 17 Training Loss: 3.563430 Validation Loss: 3.360288 Epoch: 17 Training Loss: 3.563430 Validation Loss: 3.327712 Epoch: 17 Training Loss: 3.563430 Validation Loss: 3.364418 Epoch: 17 Training Loss: 3.563430 Validation Loss: 3.346814 Epoch: 17 Training Loss: 3.563430 Validation Loss: 3.357242 Epoch: 17 Training Loss: 3.563430 Validation Loss: 3.430229 Epoch: 17 Training Loss: 3.563430 Validation Loss: 3.425789 Epoch: 17 Training Loss: 3.563430 Validation Loss: 3.446976 Epoch: 17 Training Loss: 3.563430 Validation Loss: 3.494026 Validation loss decreased (3.503493 --> 3.494026). Saving model ... Epoch 18, Batch 1 loss: 3.559523 Epoch 18, Batch 101 loss: 3.490540 Epoch: 18 Training Loss: 3.494275 Validation Loss: 3.553910 Epoch: 18 Training Loss: 3.494275 Validation Loss: 3.239473 Epoch: 18 Training Loss: 3.494275 Validation Loss: 3.080324 Epoch: 18 Training Loss: 3.494275 Validation Loss: 3.126496 Epoch: 18 Training Loss: 3.494275 Validation Loss: 3.228349 Epoch: 18 Training Loss: 3.494275 Validation Loss: 3.329998 Epoch: 18 Training Loss: 3.494275 Validation Loss: 3.317435 Epoch: 18 Training Loss: 3.494275 Validation Loss: 3.333863 Epoch: 18 Training Loss: 3.494275 Validation Loss: 3.324917 Epoch: 18 Training Loss: 3.494275 Validation Loss: 3.337744 Epoch: 18 Training Loss: 3.494275 Validation Loss: 3.408431 Epoch: 18 Training Loss: 3.494275 Validation Loss: 3.415390 Epoch: 18 Training Loss: 3.494275 Validation Loss: 3.430388 Epoch: 18 Training Loss: 3.494275 Validation Loss: 3.483956 Validation loss decreased (3.494026 --> 3.483956). Saving model ... Epoch 19, Batch 1 loss: 3.241708 Epoch 19, Batch 101 loss: 3.449950 Epoch: 19 Training Loss: 3.451965 Validation Loss: 3.611296 Epoch: 19 Training Loss: 3.451965 Validation Loss: 3.418020 Epoch: 19 Training Loss: 3.451965 Validation Loss: 3.282058 Epoch: 19 Training Loss: 3.451965 Validation Loss: 3.308052 Epoch: 19 Training Loss: 3.451965 Validation Loss: 3.332345 Epoch: 19 Training Loss: 3.451965 Validation Loss: 3.414143 Epoch: 19 Training Loss: 3.451965 Validation Loss: 3.394809 Epoch: 19 Training Loss: 3.451965 Validation Loss: 3.396017 Epoch: 19 Training Loss: 3.451965 Validation Loss: 3.359483 Epoch: 19 Training Loss: 3.451965 Validation Loss: 3.370963 Epoch: 19 Training Loss: 3.451965 Validation Loss: 3.420336 Epoch: 19 Training Loss: 3.451965 Validation Loss: 3.434786 Epoch: 19 Training Loss: 3.451965 Validation Loss: 3.456550 Epoch: 19 Training Loss: 3.451965 Validation Loss: 3.525852 Epoch 20, Batch 1 loss: 3.215693 Epoch 20, Batch 101 loss: 3.456289 Epoch: 20 Training Loss: 3.454707 Validation Loss: 3.597088 Epoch: 20 Training Loss: 3.454707 Validation Loss: 3.443948 Epoch: 20 Training Loss: 3.454707 Validation Loss: 3.255507 Epoch: 20 Training Loss: 3.454707 Validation Loss: 3.219435 Epoch: 20 Training Loss: 3.454707 Validation Loss: 3.334577 Epoch: 20 Training Loss: 3.454707 Validation Loss: 3.402046 Epoch: 20 Training Loss: 3.454707 Validation Loss: 3.351061 Epoch: 20 Training Loss: 3.454707 Validation Loss: 3.358341 Epoch: 20 Training Loss: 3.454707 Validation Loss: 3.328919 Epoch: 20 Training Loss: 3.454707 Validation Loss: 3.328539 Epoch: 20 Training Loss: 3.454707 Validation Loss: 3.388074 Epoch: 20 Training Loss: 3.454707 Validation Loss: 3.400148 Epoch: 20 Training Loss: 3.454707 Validation Loss: 3.400100 Epoch: 20 Training Loss: 3.454707 Validation Loss: 3.438776 Validation loss decreased (3.483956 --> 3.438776). Saving model ... Epoch 21, Batch 1 loss: 3.223623 Epoch 21, Batch 101 loss: 3.395471 Epoch: 21 Training Loss: 3.395226 Validation Loss: 3.677095 Epoch: 21 Training Loss: 3.395226 Validation Loss: 3.361990 Epoch: 21 Training Loss: 3.395226 Validation Loss: 3.137273 Epoch: 21 Training Loss: 3.395226 Validation Loss: 3.115492 Epoch: 21 Training Loss: 3.395226 Validation Loss: 3.199293 Epoch: 21 Training Loss: 3.395226 Validation Loss: 3.293291 Epoch: 21 Training Loss: 3.395226 Validation Loss: 3.265614 Epoch: 21 Training Loss: 3.395226 Validation Loss: 3.280005 Epoch: 21 Training Loss: 3.395226 Validation Loss: 3.266021 Epoch: 21 Training Loss: 3.395226 Validation Loss: 3.274478 Epoch: 21 Training Loss: 3.395226 Validation Loss: 3.332756 Epoch: 21 Training Loss: 3.395226 Validation Loss: 3.355375 Epoch: 21 Training Loss: 3.395226 Validation Loss: 3.369326 Epoch: 21 Training Loss: 3.395226 Validation Loss: 3.424277 Validation loss decreased (3.438776 --> 3.424277). Saving model ... Epoch 22, Batch 1 loss: 3.230278 Epoch 22, Batch 101 loss: 3.349362 Epoch: 22 Training Loss: 3.354385 Validation Loss: 3.567272 Epoch: 22 Training Loss: 3.354385 Validation Loss: 3.448125 Epoch: 22 Training Loss: 3.354385 Validation Loss: 3.207518 Epoch: 22 Training Loss: 3.354385 Validation Loss: 3.180669 Epoch: 22 Training Loss: 3.354385 Validation Loss: 3.253086 Epoch: 22 Training Loss: 3.354385 Validation Loss: 3.335770 Epoch: 22 Training Loss: 3.354385 Validation Loss: 3.306033 Epoch: 22 Training Loss: 3.354385 Validation Loss: 3.302267 Epoch: 22 Training Loss: 3.354385 Validation Loss: 3.279077 Epoch: 22 Training Loss: 3.354385 Validation Loss: 3.266189 Epoch: 22 Training Loss: 3.354385 Validation Loss: 3.323220 Epoch: 22 Training Loss: 3.354385 Validation Loss: 3.354730 Epoch: 22 Training Loss: 3.354385 Validation Loss: 3.358099 Epoch: 22 Training Loss: 3.354385 Validation Loss: 3.381601 Validation loss decreased (3.424277 --> 3.381601). Saving model ... Epoch 23, Batch 1 loss: 3.239072 Epoch 23, Batch 101 loss: 3.318901 Epoch: 23 Training Loss: 3.316135 Validation Loss: 3.505744 Epoch: 23 Training Loss: 3.316135 Validation Loss: 3.172737 Epoch: 23 Training Loss: 3.316135 Validation Loss: 3.023122 Epoch: 23 Training Loss: 3.316135 Validation Loss: 3.076792 Epoch: 23 Training Loss: 3.316135 Validation Loss: 3.133770 Epoch: 23 Training Loss: 3.316135 Validation Loss: 3.267674 Epoch: 23 Training Loss: 3.316135 Validation Loss: 3.231889 Epoch: 23 Training Loss: 3.316135 Validation Loss: 3.241336 Epoch: 23 Training Loss: 3.316135 Validation Loss: 3.242347 Epoch: 23 Training Loss: 3.316135 Validation Loss: 3.239195 Epoch: 23 Training Loss: 3.316135 Validation Loss: 3.300408 Epoch: 23 Training Loss: 3.316135 Validation Loss: 3.324809 Epoch: 23 Training Loss: 3.316135 Validation Loss: 3.346558 Epoch: 23 Training Loss: 3.316135 Validation Loss: 3.482429 Epoch 24, Batch 1 loss: 3.144686 Epoch 24, Batch 101 loss: 3.316383 Epoch: 24 Training Loss: 3.321384 Validation Loss: 3.365697 Epoch: 24 Training Loss: 3.321384 Validation Loss: 3.254682 Epoch: 24 Training Loss: 3.321384 Validation Loss: 3.134781 Epoch: 24 Training Loss: 3.321384 Validation Loss: 3.162598 Epoch: 24 Training Loss: 3.321384 Validation Loss: 3.214864 Epoch: 24 Training Loss: 3.321384 Validation Loss: 3.310085 Epoch: 24 Training Loss: 3.321384 Validation Loss: 3.284655 Epoch: 24 Training Loss: 3.321384 Validation Loss: 3.300553 Epoch: 24 Training Loss: 3.321384 Validation Loss: 3.267595 Epoch: 24 Training Loss: 3.321384 Validation Loss: 3.261749 Epoch: 24 Training Loss: 3.321384 Validation Loss: 3.298200 Epoch: 24 Training Loss: 3.321384 Validation Loss: 3.294596 Epoch: 24 Training Loss: 3.321384 Validation Loss: 3.312203 Epoch: 24 Training Loss: 3.321384 Validation Loss: 3.412631 Epoch 25, Batch 1 loss: 3.420186 Epoch 25, Batch 101 loss: 3.268543 Epoch: 25 Training Loss: 3.262777 Validation Loss: 3.543986 Epoch: 25 Training Loss: 3.262777 Validation Loss: 3.258589 Epoch: 25 Training Loss: 3.262777 Validation Loss: 3.139570 Epoch: 25 Training Loss: 3.262777 Validation Loss: 3.170215 Epoch: 25 Training Loss: 3.262777 Validation Loss: 3.252992 Epoch: 25 Training Loss: 3.262777 Validation Loss: 3.319343 Epoch: 25 Training Loss: 3.262777 Validation Loss: 3.295468 Epoch: 25 Training Loss: 3.262777 Validation Loss: 3.316490 Epoch: 25 Training Loss: 3.262777 Validation Loss: 3.286494 Epoch: 25 Training Loss: 3.262777 Validation Loss: 3.257161 Epoch: 25 Training Loss: 3.262777 Validation Loss: 3.320769 Epoch: 25 Training Loss: 3.262777 Validation Loss: 3.317002 Epoch: 25 Training Loss: 3.262777 Validation Loss: 3.317864 Epoch: 25 Training Loss: 3.262777 Validation Loss: 3.392944
(IMPLEMENTATION) Test the Model¶
Try out your model on the test dataset of dog images. Use the code cell below to calculate and print the test loss and accuracy. Ensure that your test accuracy is greater than 10%.
In [20]:
def test(loaders, model, criterion, use_cuda):
# monitor test loss and accuracy
test_loss = 0.
correct = 0.
total = 0.
model.eval()
for batch_idx, (data, target) in enumerate(loaders['test']):
# move to GPU
if use_cuda:
data, target = data.cuda(), target.cuda()
# forward pass: compute predicted outputs by passing inputs to the model
output = model(data)
# calculate the loss
loss = criterion(output, target)
# update average test loss
test_loss = test_loss + ((1 / (batch_idx + 1)) * (loss.data - test_loss))
# convert output probabilities to predicted class
pred = output.data.max(1, keepdim=True)[1]
# compare predictions to true label
correct += np.sum(np.squeeze(pred.eq(target.data.view_as(pred))).cpu().numpy())
total += data.size(0)
print('Test Loss: {:.6f}\n'.format(test_loss))
print('\nTest Accuracy: %2d%% (%2d/%2d)' % (
100. * correct / total, correct, total))
# call test function
test(loaders_scratch, model_scratch, criterion_scratch, use_cuda)
Test Loss: 3.525206 Test Accuracy: 17% (144/836)
Step 4: Create a CNN to Classify Dog Breeds (using Transfer Learning)¶
You will now use transfer learning to create a CNN that can identify dog breed from images. Your CNN must attain at least 60% accuracy on the test set.
(IMPLEMENTATION) Specify Data Loaders for the Dog Dataset¶
Use the code cell below to write three separate data loaders for the training, validation, and test datasets of dog images (located at dogImages/train, dogImages/valid, and dogImages/test, respectively).
If you like, you are welcome to use the same data loaders from the previous step, when you created a CNN from scratch.
In [21]:
## TODO: Specify data loaders
loaders_transfer = loaders_scratch
(IMPLEMENTATION) Model Architecture¶
Use transfer learning to create a CNN to classify dog breed. Use the code cell below, and save your initialized model as the variable model_transfer.
In [30]:
import torchvision.models as models
import torch.nn as nn
## TODO: Specify model architecture
model_transfer = models.resnet18(pretrained=True)
if use_cuda:
model_transfer = model_transfer.cuda()
model_transfer
Out[30]:
ResNet(
(conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(layer1): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(1): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer2): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer3): Sequential(
(0): BasicBlock(
(conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer4): Sequential(
(0): BasicBlock(
(conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(avgpool): AvgPool2d(kernel_size=7, stride=1, padding=0)
(fc): Linear(in_features=512, out_features=1000, bias=True)
)
In [31]:
numflt = model_transfer.fc.in_features
numflt
Out[31]:
512
In [32]:
model_transfer.fc.out_features
Out[32]:
1000
In [33]:
for param in model_transfer.parameters():
param.requires_grad = False
In [34]:
model_transfer.fc = nn.Linear(numflt,133)
model_transfer.fc.requires_grad = True
model_transfer
Out[34]:
ResNet(
(conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
(layer1): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
(1): BasicBlock(
(conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer2): Sequential(
(0): BasicBlock(
(conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer3): Sequential(
(0): BasicBlock(
(conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(layer4): Sequential(
(0): BasicBlock(
(conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(downsample): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): BasicBlock(
(conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(relu): ReLU(inplace)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(avgpool): AvgPool2d(kernel_size=7, stride=1, padding=0)
(fc): Linear(in_features=512, out_features=2, bias=True)
)
Question 5: Outline the steps you took to get to your final CNN architecture and your reasoning at each step. Describe why you think the architecture is suitable for the current problem.
Answer:
(IMPLEMENTATION) Specify Loss Function and Optimizer¶
Use the next code cell to specify a loss function and optimizer. Save the chosen loss function as criterion_transfer, and the optimizer as optimizer_transfer below.
In [35]:
criterion_transfer = nn.CrossEntropyLoss()
params_to_update = []
for name, param in model_transfer.named_parameters():
if param.requires_grad == True:
params_to_update.append(param)
optimizer_transfer = optim.SGD(params_to_update, lr=0.001)
(IMPLEMENTATION) Train and Validate the Model¶
Train and validate your model in the code cell below. Save the final model parameters at filepath 'model_transfer.pt'.
In [37]:
# train the model
model_transfer = train(25, loaders_transfer, model_transfer, optimizer_transfer, criterion_transfer, use_cuda, 'model_transfer.pt')
# load the model that got the best validation accuracy (uncomment the line below)
model_transfer.load_state_dict(torch.load('model_transfer.pt'))
--------------------------------------------------------------------------- RuntimeError Traceback (most recent call last) <ipython-input-37-14bde0bd5b4c> in <module>() 1 # train the model ----> 2 model_transfer = train(25, loaders_transfer, model_transfer, optimizer_transfer, criterion_transfer, use_cuda, 'model_transfer.pt') 3 4 # load the model that got the best validation accuracy (uncomment the line below) 5 model_transfer.load_state_dict(torch.load('model_transfer.pt')) <ipython-input-19-bc95da49705b> in train(n_epochs, loaders, model, optimizer, criterion, use_cuda, save_path) 21 ## record the average training loss, using something like 22 ## train_loss = train_loss + ((1 / (batch_idx + 1)) * (loss.data - train_loss)) ---> 23 output = model(data) 24 loss = criterion_scratch(output,target) 25 loss.backward() /opt/conda/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs) 489 result = self._slow_forward(*input, **kwargs) 490 else: --> 491 result = self.forward(*input, **kwargs) 492 for hook in self._forward_hooks.values(): 493 hook_result = hook(self, input, result) /opt/conda/lib/python3.6/site-packages/torchvision-0.2.1-py3.6.egg/torchvision/models/resnet.py in forward(self, x) 149 x = self.avgpool(x) 150 x = x.view(x.size(0), -1) --> 151 x = self.fc(x) 152 153 return x /opt/conda/lib/python3.6/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs) 489 result = self._slow_forward(*input, **kwargs) 490 else: --> 491 result = self.forward(*input, **kwargs) 492 for hook in self._forward_hooks.values(): 493 hook_result = hook(self, input, result) /opt/conda/lib/python3.6/site-packages/torch/nn/modules/linear.py in forward(self, input) 53 54 def forward(self, input): ---> 55 return F.linear(input, self.weight, self.bias) 56 57 def extra_repr(self): /opt/conda/lib/python3.6/site-packages/torch/nn/functional.py in linear(input, weight, bias) 990 if input.dim() == 2 and bias is not None: 991 # fused op is marginally faster --> 992 return torch.addmm(bias, input, weight.t()) 993 994 output = input.matmul(weight.t()) RuntimeError: Expected object of type torch.FloatTensor but found type torch.cuda.FloatTensor for argument #4 'mat1'
(IMPLEMENTATION) Test the Model¶
Try out your model on the test dataset of dog images. Use the code cell below to calculate and print the test loss and accuracy. Ensure that your test accuracy is greater than 60%.
In [ ]:
test(loaders_transfer, model_transfer, criterion_transfer, use_cuda)
(IMPLEMENTATION) Predict Dog Breed with the Model¶
Write a function that takes an image path as input and returns the dog breed (Affenpinscher, Afghan hound, etc) that is predicted by your model.
In [ ]:
### TODO: Write a function that takes a path to an image as input
### and returns the dog breed that is predicted by the model.
# list of class names by index, i.e. a name can be accessed like class_names[0]
class_names = [item[4:].replace("_", " ") for item in data_transfer['train'].classes]
def predict_breed_transfer(img_path):
# load the image and return the predicted breed
return None
Step 5: Write your Algorithm¶
Write an algorithm that accepts a file path to an image and first determines whether the image contains a human, dog, or neither. Then,
- if a dog is detected in the image, return the predicted breed.
- if a human is detected in the image, return the resembling dog breed.
- if neither is detected in the image, provide output that indicates an error.
You are welcome to write your own functions for detecting humans and dogs in images, but feel free to use the face_detector and human_detector functions developed above. You are required to use your CNN from Step 4 to predict dog breed.
Some sample output for our algorithm is provided below, but feel free to design your own user experience!
(IMPLEMENTATION) Write your Algorithm¶
In [ ]:
### TODO: Write your algorithm.
### Feel free to use as many code cells as needed.
def run_app(img_path):
## handle cases for a human face, dog, and neither
Step 6: Test Your Algorithm¶
In this section, you will take your new algorithm for a spin! What kind of dog does the algorithm think that you look like? If you have a dog, does it predict your dog's breed accurately? If you have a cat, does it mistakenly think that your cat is a dog?
(IMPLEMENTATION) Test Your Algorithm on Sample Images!¶
Test your algorithm at least six images on your computer. Feel free to use any images you like. Use at least two human and two dog images.
Question 6: Is the output better than you expected :) ? Or worse :( ? Provide at least three possible points of improvement for your algorithm.
Answer: (Three possible points for improvement)
In [ ]:
## TODO: Execute your algorithm from Step 6 on
## at least 6 images on your computer.
## Feel free to use as many code cells as needed.
## suggested code, below
for file in np.hstack((human_files[:3], dog_files[:3])):
run_app(file)